我们从Python开源项目中，提取了以下25个代码示例，用于说明如何使用matplotlib.pylab.tight_layout()。
def plot(params_dir): model_dirs = [name for name in os.listdir(params_dir) if os.path.isdir(os.path.join(params_dir, name))] df = defaultdict(list) for model_dir in model_dirs: df[re.sub('_bin_scaled_mono_True_ratio', '', model_dir)] = [ dd.io.load(path)['best_epoch']['validate_objective'] for path in glob.glob(os.path.join( params_dir, model_dir) + '/*.h5')] df = pd.DataFrame(dict([(k, pd.Series(v)) for k, v in df.iteritems()])) df.to_csv(os.path.basename(os.path.normpath(params_dir))) plt.figure(figsize=(16, 4), dpi=300) g = sns.boxplot(df) g.set_xticklabels(df.columns, rotation=45) plt.tight_layout() plt.savefig('{}_errors_box_plot.png'.format( os.path.join(IMAGES_DIRECTORY, os.path.basename(os.path.normpath(params_dir)))))
def show_bars_over_time( task_output_path=None, query_laps=[0, 1, 2, 5, None], ncols=10): ''' ''' nrows = len(query_laps) fig_handle, ax_handles_RC = pylab.subplots( figsize=(SMALL_FIG_SIZE[0] * ncols, SMALL_FIG_SIZE[1] * nrows), nrows=nrows, ncols=ncols, sharex=True, sharey=True) for row_id, lap_val in enumerate(query_laps): cur_model, lap_val = bnpy.load_model_at_lap(task_output_path, lap_val) cur_topics_KV = cur_model.obsModel.getTopics() # Plot the current model cur_ax_list = ax_handles_RC[row_id].flatten().tolist() bnpy.viz.BarsViz.show_square_images( cur_topics_KV, vmin=0.0, vmax=0.06, ax_list=cur_ax_list) cur_ax_list[0].set_ylabel("lap: %d" % lap_val) pylab.tight_layout() ############################################################################### # # Show the clusters over time
def showExampleDocs(pylab=None, nrows=3, ncols=3): if pylab is None: from matplotlib import pylab Data = get_data(seed=0, nObsPerDoc=200) PRNG = np.random.RandomState(0) chosenDocs = PRNG.choice(Data.nDoc, nrows * ncols, replace=False) for ii, d in enumerate(chosenDocs): start = Data.doc_range[d] stop = Data.doc_range[d + 1] Xd = Data.X[start:stop] pylab.subplot(nrows, ncols, ii + 1) pylab.plot(Xd[:, 0], Xd[:, 1], 'k.') pylab.axis('image') pylab.xlim([-1.5, 1.5]) pylab.ylim([-1.5, 1.5]) pylab.xticks([]) pylab.yticks([]) pylab.tight_layout() # Set Toy Parameters ###########################################################
def save(self, out_path): '''Saves a figure for the monitor Args: out_path: str ''' plt.clf() np.set_printoptions(precision=4) font = { 'size': 7 } matplotlib.rc('font', **font) y = 2 x = ((len(self.d) - 1) // y) + 1 fig, axes = plt.subplots(y, x) fig.set_size_inches(20, 8) for j, (k, v) in enumerate(self.d.iteritems()): ax = axes[j // x, j % x] ax.plot(v, label=k) if k in self.d_valid.keys(): ax.plot(self.d_valid[k], label=k + '(valid)') ax.set_title(k) ax.legend() plt.tight_layout() plt.savefig(out_path, facecolor=(1, 1, 1)) plt.close()
def plot1D_mat(a, b, M, title=''): """ Plot matrix M with the source and target 1D distribution Creates a subplot with the source distribution a on the left and target distribution b on the tot. The matrix M is shown in between. Parameters ---------- a : np.array, shape (na,) Source distribution b : np.array, shape (nb,) Target distribution M : np.array, shape (na,nb) Matrix to plot """ na, nb = M.shape gs = gridspec.GridSpec(3, 3) xa = np.arange(na) xb = np.arange(nb) ax1 = pl.subplot(gs[0, 1:]) pl.plot(xb, b, 'r', label='Target distribution') pl.yticks(()) pl.title(title) ax2 = pl.subplot(gs[1:, 0]) pl.plot(a, xa, 'b', label='Source distribution') pl.gca().invert_xaxis() pl.gca().invert_yaxis() pl.xticks(()) pl.subplot(gs[1:, 1:], sharex=ax1, sharey=ax2) pl.imshow(M, interpolation='nearest') pl.axis('off') pl.xlim((0, nb)) pl.tight_layout() pl.subplots_adjust(wspace=0., hspace=0.2)
def run_frey(): # import dataset data = pods.datasets.brendan_faces() # Y = data['Y'][:50, :] Y = data['Y'] Yn = Y - np.mean(Y, axis=0) Yn /= np.std(Y, axis=0) Y = Yn # inference print "inference ..." M = 30 D = 20 lvm = vfe.SGPLVM(Y, D, M, lik='Gaussian') lvm.optimise(method='L-BFGS-B', maxiter=10) plt.figure() mx, vx = lvm.get_posterior_x() zu = lvm.sgp_layer.zu plt.scatter(mx[:, 0], mx[:, 1]) plt.plot(zu[:, 0], zu[:, 1], 'ko') nx = ny = 30 x_values = np.linspace(-5, 5, nx) y_values = np.linspace(-5, 5, ny) sx = 28 sy = 20 canvas = np.empty((sx * ny, sy * nx)) for i, yi in enumerate(x_values): for j, xi in enumerate(y_values): z_mu = np.array([[xi, yi]]) x_mean, x_var = lvm.predict_f(z_mu) canvas[(nx - i - 1) * sx:(nx - i) * sx, j * sy:(j + 1) * sy] = x_mean.reshape(sx, sy) plt.figure(figsize=(8, 10)) Xi, Yi = np.meshgrid(x_values, y_values) plt.imshow(canvas, origin="upper", cmap="gray") plt.tight_layout() plt.show()
def run_frey(): # import dataset data = pods.datasets.brendan_faces() # Y = data['Y'][:50, :] Y = data['Y'] Yn = Y - np.mean(Y, axis=0) Yn /= np.std(Y, axis=0) Y = Yn # inference print "inference ..." M = 30 D = 20 lvm = aep.SGPLVM(Y, D, M, lik='Gaussian') # lvm.train(alpha=0.5, no_epochs=10, n_per_mb=100, lrate=0.1, fixed_params=['sn']) lvm.optimise(method='L-BFGS-B', alpha=0.1, maxiter=10) plt.figure() mx, vx = lvm.get_posterior_x() zu = lvm.sgp_layer.zu plt.scatter(mx[:, 0], mx[:, 1]) plt.plot(zu[:, 0], zu[:, 1], 'ko') nx = ny = 30 x_values = np.linspace(-5, 5, nx) y_values = np.linspace(-5, 5, ny) sx = 28 sy = 20 canvas = np.empty((sx * ny, sy * nx)) for i, yi in enumerate(x_values): for j, xi in enumerate(y_values): z_mu = np.array([[xi, yi]]) x_mean, x_var = lvm.predict_f(z_mu) canvas[(nx - i - 1) * sx:(nx - i) * sx, j * sy:(j + 1) * sy] = x_mean.reshape(sx, sy) plt.figure(figsize=(8, 10)) Xi, Yi = np.meshgrid(x_values, y_values) plt.imshow(canvas, origin="upper", cmap="gray") plt.tight_layout() plt.show()
def plot_graphs(df, trending_daily, day_from, day_to, limit, country_code, folder_out=None): days = pd.DatetimeIndex(start=day_from, end=day_to, freq='D') for day in days: fig = plt.figure() ax = fig.add_subplot(111) plt.rc('lines', linewidth=2) data = trending_daily.get_group(str(day.date())) places, clusters = top_trending(data, limit) for cluster in clusters: places.add(max_from_cluster(cluster, data)) ax.set_prop_cycle(plt.cycler('color', ['r', 'b', 'yellow'] + [plt.cm.Accent(i) for i in np.linspace(0, 1, limit-3)] ) + plt.cycler('linestyle', ['-', '-', '-', '-', '-', '--', '--', '--', '--', '--'])) frame = export(places, clusters, data) frame.sort_values('trending_rank', ascending=False, inplace=True) for i in range(len(frame)): item = frame.index[i] lat, lon, country = item result_items = ReverseGeoCode().get_address_attributes(lat, lon, 10, 'city', 'country_code') if 'city' not in result_items.keys(): mark = "%s (%s)" % (manipulate_display_name(result_items['display_name']), result_items['country_code'].upper() if 'country_code' in result_items.keys() else country) else: if check_eng(result_items['city']): mark = "%s (%s)" % (result_items['city'], result_items['country_code'].upper()) else: mark = "%.2f %.2f (%s)" % (lat, lon, result_items['country_code'].upper()) gp = df.loc[item].plot(ax=ax, x='date', y='count', label=mark) ax.tick_params(axis='both', which='major', labelsize=10) ax.set_yscale("log", nonposy='clip') plt.xlabel('Date', fontsize='small', verticalalignment='baseline', horizontalalignment='right') plt.ylabel('Total number of views (log)', fontsize='small', verticalalignment='center', horizontalalignment='center', labelpad=6) gp.legend(loc='best', fontsize='xx-small', ncol=2) gp.set_title('Top 10 OSM trending places on ' + str(day.date()), {'fontsize': 'large', 'verticalalignment': 'bottom'}) plt.tight_layout() db = TrendingDb() db.update_table_img(plt, str(day.date()), region=country_code) plt.close()
def main(args_dict): # Set up plot matplotlib_configure_as_notebook() fig, ax = plt.subplots(1, 2, facecolor='w', figsize=(9.25, 3.25)) # Estimating Z Ms = np.arange(3, args_dict['M']+1) ax[0].set_xlabel('number of samples $M$') ax[0].set_ylabel('MSE of $\hat{Z}$, in units of $Z^2$') ax[0].set_xlim((np.min(Ms), np.max(Ms))) ax[0].set_xscale('log') ax[0].set_yscale('log') ax[0].grid(b=True, which='major', linestyle='dotted', lw=.5, color='black', alpha=0.5) ax[0].plot(Ms, Z_Gumbel_MSE(Ms), linestyle='-', color=tableau20(0), label='Gumbel: MSE') ax[0].plot(Ms, Z_Gumbel_var(Ms), linestyle='dashed', color=tableau20(0), label='Gumbel: var') ax[0].plot(Ms, Z_Exponential_MSE(Ms), linestyle='-', color=tableau20(2), label='Exponential: MSE') ax[0].plot(Ms, Z_Exponential_var(Ms), linestyle='dashed', color=tableau20(2), label='Exponential: var') # Estimating ln Z Ms = np.arange(1, args_dict['M']+1) ax[1].set_xlabel('number of samples $M$') ax[1].set_ylabel('MSE of $\widehat{\ln Z}$, in units of $1$') ax[1].set_xlim((np.min(Ms), np.max(Ms))) ax[1].set_xscale('log') ax[1].set_yscale('log') ax[1].grid(b=True, which='major', linestyle='dotted', lw=0.5, color='black', alpha=0.5) ax[1].plot(Ms, lnZ_Gumbel_MSE(Ms), linestyle='-', color=tableau20(0), label='Gumbel: MSE') ax[1].plot(Ms, lnZ_Exponential_MSE(Ms), linestyle='-', color=tableau20(2), label='Exponential: MSE') ax[1].plot(Ms, lnZ_Exponential_var(Ms), linestyle='dashed', color=tableau20(2), label='Exponential: var') # Finalize plot lgd0 = ax[0].legend() lgd1 = ax[1].legend() plt.tight_layout() save_plot(fig, 'figures/fig1', (lgd0, lgd1,))
def show_clusters_over_time( task_output_path=None, query_laps=[0, 1, 2, 5, 10, None], nrows=2): ''' Read model snapshots from provided folder and make visualizations Post Condition -------------- New matplotlib plot with some nice pictures. ''' ncols = int(np.ceil(len(query_laps) // float(nrows))) fig_handle, ax_handle_list = pylab.subplots( figsize=(FIG_SIZE[0] * ncols, FIG_SIZE[1] * nrows), nrows=nrows, ncols=ncols, sharex=True, sharey=True) for plot_id, lap_val in enumerate(query_laps): cur_model, lap_val = bnpy.load_model_at_lap(task_output_path, lap_val) # Plot the current model cur_ax_handle = ax_handle_list.flatten()[plot_id] bnpy.viz.PlotComps.plotCompsFromHModel( cur_model, Data=dataset, ax_handle=cur_ax_handle) cur_ax_handle.set_xticks([-2, -1, 0, 1, 2]) cur_ax_handle.set_yticks([-2, -1, 0, 1, 2]) cur_ax_handle.set_xlabel("lap: %d" % lap_val) pylab.tight_layout() ############################################################################### # Training from K=1 cluster # ------------------------- # # Using 1 initial cluster, with birth and merge proposal moves.
def show_clusters_over_time( task_output_path=None, query_laps=[0, 1, 2, 5, 10, None], nrows=2): ''' Read model snapshots from provided folder and make visualizations Post Condition -------------- New matplotlib plot with some nice pictures. ''' ncols = int(np.ceil(len(query_laps) // float(nrows))) fig_handle, ax_handle_list = pylab.subplots( figsize=(FIG_SIZE[0] * ncols, FIG_SIZE[1] * nrows), nrows=nrows, ncols=ncols, sharex=True, sharey=True) for plot_id, lap_val in enumerate(query_laps): cur_model, lap_val = bnpy.load_model_at_lap(task_output_path, lap_val) # Plot the current model cur_ax_handle = ax_handle_list.flatten()[plot_id] bnpy.viz.PlotComps.plotCompsFromHModel( cur_model, Data=dataset, ax_handle=cur_ax_handle) cur_ax_handle.set_xticks([-2, -1, 0, 1, 2]) cur_ax_handle.set_yticks([-2, -1, 0, 1, 2]) cur_ax_handle.set_xlabel("lap: %d" % lap_val) pylab.tight_layout() ############################################################################### # # Show the estimated clusters over time
def show_clusters_over_time( task_output_path=None, query_laps=[0, 1, 2, 10, 20, None], nrows=2): ''' ''' ncols = int(np.ceil(len(query_laps) // float(nrows))) fig_handle, ax_handle_list = pylab.subplots( figsize=(FIG_SIZE[0] * ncols, FIG_SIZE[1] * nrows), nrows=nrows, ncols=ncols, sharex=True, sharey=True) for plot_id, lap_val in enumerate(query_laps): cur_model, lap_val = bnpy.load_model_at_lap(task_output_path, lap_val) cur_ax_handle = ax_handle_list.flatten()[plot_id] bnpy.viz.PlotComps.plotCompsFromHModel( cur_model, dataset=dataset, ax_handle=cur_ax_handle) cur_ax_handle.set_xlim([-4.5, 4.5]) cur_ax_handle.set_xlabel("lap: %d" % lap_val) pylab.tight_layout() ############################################################################### # # Run with *merge* moves only, from K=5 initial clusters # -------------------------------------------------------- # # Unfortunately, no pairwise merge is accepted. # The model is stuck using 5 clusters when one cluster would do.
def show_single_sequence(seq_id): start = dataset.doc_range[seq_id] stop = dataset.doc_range[seq_id + 1] for dim in xrange(12): X_seq = dataset.X[start:stop] pylab.plot(X_seq[:, dim], '.-') pylab.xlabel('time') pylab.ylabel('angle') pylab.tight_layout() ############################################################################### # # Visualization of the first sequence # -----------------------------------
def show_top_words_over_time( task_output_path=None, vocabList=None, query_laps=[0, 1, 2, 5, None], ncols=10): ''' ''' nrows = len(query_laps) fig_handle, ax_handles_RC = pylab.subplots( figsize=(SMALL_FIG_SIZE[0] * ncols, SMALL_FIG_SIZE[1] * nrows), nrows=nrows, ncols=ncols, sharex=True, sharey=True) for row_id, lap_val in enumerate(query_laps): cur_model, lap_val = bnpy.load_model_at_lap(task_output_path, lap_val) # Plot the current model cur_ax_list = ax_handles_RC[row_id].flatten().tolist() bnpy.viz.PrintTopics.plotCompsFromHModel( cur_model, vocabList=vocabList, fontsize=9, Ktop=7, ax_list=cur_ax_list) cur_ax_list[0].set_ylabel("lap: %d" % lap_val) pylab.subplots_adjust( wspace=0.04, hspace=0.1, left=0.01, right=0.99, top=0.99, bottom=0.1) pylab.tight_layout() ############################################################################### # # Show the topics over time
def show_clusters_over_time( task_output_path=None, query_laps=[0, 1, 2, 10, 20, None], nrows=2): ''' ''' ncols = int(np.ceil(len(query_laps) // float(nrows))) fig_handle, ax_handle_list = pylab.subplots( figsize=(FIG_SIZE[0] * ncols, FIG_SIZE[1] * nrows), nrows=nrows, ncols=ncols, sharex=True, sharey=True) for plot_id, lap_val in enumerate(query_laps): cur_model, lap_val = bnpy.load_model_at_lap(task_output_path, lap_val) cur_ax_handle = ax_handle_list.flatten()[plot_id] bnpy.viz.PlotComps.plotCompsFromHModel( cur_model, dataset=dataset, ax_handle=cur_ax_handle) cur_ax_handle.set_title("lap: %d" % lap_val) cur_ax_handle.set_xlabel(dataset.column_names[0]) cur_ax_handle.set_ylabel(dataset.column_names[1]) cur_ax_handle.set_xlim(data_ax_h.get_xlim()) cur_ax_handle.set_ylim(data_ax_h.get_ylim()) pylab.tight_layout() ############################################################################### # # *DiagGauss* observation model, without moves # -------------------------------------------- # # Start with too many clusters (K=25)
def show_clusters_over_time( task_output_path=None, query_laps=[0, 1, 2, 10, 20, None], nrows=2): ''' Show 2D elliptical contours overlaid on raw data. ''' ncols = int(np.ceil(len(query_laps) // float(nrows))) fig_handle, ax_handle_list = pylab.subplots( figsize=(SMALL_FIG_SIZE[0] * ncols, SMALL_FIG_SIZE[1] * nrows), nrows=nrows, ncols=ncols, sharex=True, sharey=True) for plot_id, lap_val in enumerate(query_laps): cur_model, lap_val = bnpy.load_model_at_lap(task_output_path, lap_val) cur_ax_handle = ax_handle_list.flatten()[plot_id] bnpy.viz.PlotComps.plotCompsFromHModel( cur_model, dataset=dataset, ax_handle=cur_ax_handle) cur_ax_handle.set_title("lap: %d" % lap_val) cur_ax_handle.set_xlabel(dataset.column_names[0]) cur_ax_handle.set_ylabel(dataset.column_names[1]) cur_ax_handle.set_xlim(data_ax_h.get_xlim()) cur_ax_handle.set_ylim(data_ax_h.get_ylim()) pylab.tight_layout() ############################################################################### # # *DiagGauss* observation model # ----------------------------- # # Assume diagonal covariances. # # Start with too many clusters (K=20)
def show_bars_over_time( task_output_path=None, query_laps=[0, 1, 2, 5, None], ncols=10): ''' ''' nrows = len(query_laps) fig_handle, ax_handles_RC = pylab.subplots( figsize=(SMALL_FIG_SIZE[0] * ncols, SMALL_FIG_SIZE[1] * nrows), nrows=nrows, ncols=ncols, sharex=True, sharey=True) for row_id, lap_val in enumerate(query_laps): cur_model, lap_val = bnpy.load_model_at_lap(task_output_path, lap_val) cur_topics_KV = cur_model.obsModel.getTopics() # Plot the current model cur_ax_list = ax_handles_RC[row_id].flatten().tolist() bnpy.viz.BarsViz.show_square_images( cur_topics_KV, vmin=0.0, vmax=0.1, ax_list=cur_ax_list) cur_ax_list[0].set_ylabel("lap: %d" % lap_val) pylab.tight_layout() ############################################################################### # From K=2 initial clusters # ------------------------- # # Using random initialization
def show_bars_over_time( task_output_path=None, query_laps=[0, 1, 2, 5, None], ncols=10): ''' Show square-image visualization of estimated topics over time. Post Condition -------------- New matplotlib figure with visualization (one row per lap). ''' nrows = len(query_laps) fig_handle, ax_handles_RC = pylab.subplots( figsize=(SMALL_FIG_SIZE[0] * ncols, SMALL_FIG_SIZE[1] * nrows), nrows=nrows, ncols=ncols, sharex=True, sharey=True) for row_id, lap_val in enumerate(query_laps): cur_model, lap_val = bnpy.load_model_at_lap(task_output_path, lap_val) cur_topics_KV = cur_model.obsModel.getTopics() # Plot the current model cur_ax_list = ax_handles_RC[row_id].flatten().tolist() bnpy.viz.BarsViz.show_square_images( cur_topics_KV, vmin=0.0, vmax=0.06, ax_list=cur_ax_list) cur_ax_list[0].set_ylabel("lap: %d" % lap_val) pylab.tight_layout() ############################################################################### # # Examine the bars over time
def show_bars_over_time( task_output_path=None, query_laps=[0, 1, 2, 5, None], ncols=10): ''' ''' nrows = len(query_laps) fig_handle, ax_handles_RC = pylab.subplots( figsize=(SMALL_FIG_SIZE[0] * ncols, SMALL_FIG_SIZE[1] * nrows), nrows=nrows, ncols=ncols, sharex=True, sharey=True) for row_id, lap_val in enumerate(query_laps): cur_model, lap_val = bnpy.load_model_at_lap(task_output_path, lap_val) cur_topics_KV = cur_model.obsModel.getTopics() # Plot the current model cur_ax_list = ax_handles_RC[row_id].flatten().tolist() bnpy.viz.BarsViz.show_square_images( cur_topics_KV, vmin=0.0, vmax=0.06, ax_list=cur_ax_list) cur_ax_list[0].set_ylabel("lap: %d" % lap_val) pylab.tight_layout() ############################################################################### # Train LDA topic model # --------------------- # # Using 10 clusters and the 'randexamples' initialization procedure.
def run_mnist(): np.random.seed(42) # import dataset f = gzip.open('./tmp/data/mnist.pkl.gz', 'rb') (x_train, t_train), (x_valid, t_valid), (x_test, t_test) = cPickle.load(f) f.close() Y = x_train[:100, :] labels = t_train[:100] Y[Y < 0.5] = -1 Y[Y > 0.5] = 1 # inference print "inference ..." M = 30 D = 2 # lvm = vfe.SGPLVM(Y, D, M, lik='Gaussian') lvm = vfe.SGPLVM(Y, D, M, lik='Probit') # lvm.train(alpha=0.5, no_epochs=10, n_per_mb=100, lrate=0.1, fixed_params=['sn']) lvm.optimise(method='L-BFGS-B') plt.figure() mx, vx = lvm.get_posterior_x() zu = lvm.sgp_layer.zu plt.scatter(mx[:, 0], mx[:, 1], c=labels) plt.plot(zu[:, 0], zu[:, 1], 'ko') nx = ny = 30 x_values = np.linspace(-5, 5, nx) y_values = np.linspace(-5, 5, ny) sx = 28 sy = 28 canvas = np.empty((sx * ny, sy * nx)) for i, yi in enumerate(x_values): for j, xi in enumerate(y_values): z_mu = np.array([[xi, yi]]) x_mean, x_var = lvm.predict_f(z_mu) t = x_mean / np.sqrt(1 + x_var) Z = 0.5 * (1 + special.erf(t / np.sqrt(2))) canvas[(nx - i - 1) * sx:(nx - i) * sx, j * sy:(j + 1) * sy] = Z.reshape(sx, sy) plt.figure(figsize=(8, 10)) Xi, Yi = np.meshgrid(x_values, y_values) plt.imshow(canvas, origin="upper", cmap="gray") plt.tight_layout() plt.show()
def run_mnist(): np.random.seed(42) # import dataset f = gzip.open('./tmp/data/mnist.pkl.gz', 'rb') (x_train, t_train), (x_valid, t_valid), (x_test, t_test) = cPickle.load(f) f.close() Y = x_train[:100, :] labels = t_train[:100] Y[Y < 0.5] = -1 Y[Y > 0.5] = 1 # inference print "inference ..." M = 30 D = 2 # lvm = aep.SGPLVM(Y, D, M, lik='Gaussian') lvm = aep.SGPLVM(Y, D, M, lik='Probit') # lvm.train(alpha=0.5, no_epochs=10, n_per_mb=100, lrate=0.1, fixed_params=['sn']) lvm.optimise(method='L-BFGS-B', alpha=0.1) plt.figure() mx, vx = lvm.get_posterior_x() zu = lvm.sgp_layer.zu plt.scatter(mx[:, 0], mx[:, 1], c=labels) plt.plot(zu[:, 0], zu[:, 1], 'ko') nx = ny = 30 x_values = np.linspace(-5, 5, nx) y_values = np.linspace(-5, 5, ny) sx = 28 sy = 28 canvas = np.empty((sx * ny, sy * nx)) for i, yi in enumerate(x_values): for j, xi in enumerate(y_values): z_mu = np.array([[xi, yi]]) x_mean, x_var = lvm.predict_f(z_mu) t = x_mean / np.sqrt(1 + x_var) Z = 0.5 * (1 + special.erf(t / np.sqrt(2))) canvas[(nx - i - 1) * sx:(nx - i) * sx, j * sy:(j + 1) * sy] = Z.reshape(sx, sy) plt.figure(figsize=(8, 10)) Xi, Yi = np.meshgrid(x_values, y_values) plt.imshow(canvas, origin="upper", cmap="gray") plt.tight_layout() plt.show()
def main(args_dict): # Extract configuration from command line arguments Ms = np.array(args_dict['Ms']) alphas = np.linspace(args_dict['alpha_min'], args_dict['alpha_max'], args_dict['alpha_num']) K = args_dict['K'] do_confidence = args_dict['confidence'] # Estimate MSEs by sampling print('Estimating MSE of estimators of Z...') MSEs_Z, MSE_stdevs_Z = estimate_MSE_vs_alpha(lambda x: x, Ms, alphas, K) print('Estimating MSE of estimators of ln(Z)...') MSEs_lnZ, MSE_stdevs_lnZ = estimate_MSE_vs_alpha(np.log, Ms, alphas, K) # Set up plot matplotlib_configure_as_notebook() fig = plt.figure(facecolor='w', figsize=(8.25, 3.25)) gs = gridspec.GridSpec(1, 3, width_ratios=[1.0, 1.0, 0.5]) ax = [plt.subplot(gs[0]), plt.subplot(gs[2]), plt.subplot(gs[1])] ax[0].set_xlabel('$\\alpha$') ax[2].set_xlabel('$\\alpha$') ax[0].set_ylabel('MSE of estimators of $Z$, in units of $Z^2$') ax[2].set_ylabel('MSE of estimators of $\ln Z$, in units of $1$') colors = [plt.cm.plasma(0.8 - 1.0 * i / len(Ms)) for i in xrange(len(Ms))] # Gumbel (alpha=0) and Exponential (alpha=1) tricks can be handled analytically legend_Gumbel = 'Gumbel trick\n($\\alpha=0$, theoretical)' legend_Exponential = 'Exponential trick\n($\\alpha=1$, theoretical)' ax[0].scatter(np.zeros(len(Ms)), Z_Gumbel_MSE(Ms), marker='o', color=colors, label=legend_Gumbel) ax[0].scatter(np.ones(len(Ms)), Z_Exponential_MSE(Ms), marker='^', color=colors, label=legend_Exponential) ax[2].scatter(np.zeros(len(Ms)), lnZ_Gumbel_MSE(Ms), marker='o', color=colors, label=legend_Gumbel) ax[2].scatter(np.ones(len(Ms)), lnZ_Exponential_MSE(Ms), marker='^', color=colors, label=legend_Exponential) # Remaining tricks MSE were estimated by sampling labels = ['$M=%d$' % (M) for M in Ms] plot_MSEs_to_axis(ax[0], alphas, MSEs_Z, MSE_stdevs_Z, do_confidence, labels, colors) plot_MSEs_to_axis(ax[2], alphas, MSEs_lnZ, MSE_stdevs_lnZ, do_confidence, labels, colors) # Finalize plot ax[0].set_ylim((5*1e-3, 10)) ax[2].set_ylim((5*1e-3, 10)) handles, labels = ax[0].get_legend_handles_labels() remove_chartjunk(ax[1]) ax[1].spines["bottom"].set_visible(False) ax[1].tick_params(axis="both", which="both", bottom="off", top="off", labelbottom="off", left="off", right="off", labelleft="off") ax[1].legend(handles, labels, frameon=False, loc='upper center', bbox_to_anchor=[0.44, 1.05]) plt.tight_layout() save_plot(fig, 'figures/fig2_K%d' % (K))
def show_many_random_initial_models( obsPriorArgsDict, initArgsDict, nrows=1, ncols=6): ''' Create plot of many different random initializations ''' fig_handle, ax_handle_list = pylab.subplots( figsize=(SMALL_FIG_SIZE[0] * ncols, SMALL_FIG_SIZE[1] * nrows), nrows=nrows, ncols=ncols, sharex=True, sharey=True) for trial_id in range(nrows * ncols): cur_model = bnpy.make_initialized_model( dataset, allocModelName='FiniteMixtureModel', obsModelName='Gauss', algName='VB', allocPriorArgsDict=dict(gamma=10.0), obsPriorArgsDict=obsPriorArgsDict, initArgsDict=initArgsDict, seed=int(trial_id), ) # Plot the current model cur_ax_handle = ax_handle_list.flatten()[trial_id] bnpy.viz.PlotComps.plotCompsFromHModel( cur_model, Data=dataset, ax_handle=cur_ax_handle) cur_ax_handle.set_xticks([-2, -1, 0, 1, 2]) cur_ax_handle.set_yticks([-2, -1, 0, 1, 2]) pylab.tight_layout() ############################################################################### # initname: 'randexamples' # ------------------------ # This procedure selects K examples uniformly at random. # Each cluster is then initialized from one selected example, # using a standard global step update. # # **Example 1**: # Initialize with 8 clusters, with prior biased towards small covariances # # .. math:: # # \E_{\mbox{prior}}[ \Sigma_k ] = 0.01 I_D
